The present invention relates to image classification for disease prediction, and more specifically, to an automatic clinical workflow that recognizes and analyzes 2D and Doppler modality echocardiogram images for automated measurements and the diagnosis, prediction and prognosis of heart disease.
Cardiovascular disease including heart failure is a major health problem accounting for about 30% of human deaths worldwide. Heart failure is also the leading cause of hospitalization in adults over the age of 65 years. Echocardiography is an important diagnostic aid in cardiology for the morphological and functional assessment of the heart. In a typical patient echocardiogram (echo) examination, a clinician called a sonographer places an ultrasound device against the patient's chest to capture a number of 2D images/videos of the patients' heart. Reflected sound waves reveal the inner structure of the heart walls and the velocities of blood flows. The ultrasound device position is varied during an echo exam to capture different anatomical sections as 2D slices of the heart from different viewpoints or views. The clinician has the option of adding to these 2D images a waveform captured from various possible modalities including continuous wave Doppler, m-mode, pulsed wave Doppler and pulsed wave tissue Doppler. The 2D images/videos and Doppler modality images are typically saved in DICOM (Digital Imaging and Communications in Medicine) formatted files. Although the type of modality is partially indicated in the metadata of the DICOM file, the ultrasound device position in both the modality and 2D views, which is the final determinant of which cardiac structure has been imaged, is not.
After the patient examination, a clinician/technician goes through the DICOM files, manually annotates heart chambers and structures like the left ventricle (LV) and takes measurements of those structures. The process is reliant on the clinicians' training to recognize the view in each image and make the appropriate measurements. In a follow up examination, a cardiologist reviews the DICOM images and measurements, compares them to memorized guideline values and make a diagnosis based on the interpretation made from the echocardiogram.
The current workflow process for analyzing DICOM images, measuring cardiac structures in the images and determining, predicting and prognosticating heart disease is highly manual, time-consuming and error-prone.
There has been a recent proposal for automated cardiac image interpretation to enable low cost assessment of cardiac function by non-experts. Although the proposed automated system holds the promise of improved performance compared to the manual process, the system has several shortfalls. One shortfall is that the system only recognizes 2D images. In addition, although the proposed system may distinguish between a normal heart and a diseased heart, the proposed system is incapable of distinguishing hearts having similar-looking diseases. Consequently, the number of heart diseases identified by the proposed system is very limited and requires manual intervention to identify other types of heart diseases.
For example, heart failure has been traditionally viewed as a failure of contractile function and left ventricular ejection fraction (LVEF) has been widely used to define systolic function, assess prognosis and select patients for therapeutic interventions. However, it is recognized that heart failure can occur in the presence of normal or near-normal EF: so-called “heart failure with preserved ejection fraction (HFPEF)” which accounts for a substantial proportion of clinical cases of heart failure. Heart failure with severe dilation and/or markedly reduced EF: so-called “heart failure with reduced ejection fraction (HFREF)” is the best understood type of heart failure in terms of pathophysiology and treatment. The symptoms of heart failure may develop suddenly ‘acute heart failure’ leading to hospital admission, but they can also develop gradually. Timely diagnosis, categorization of heart failure subtype-HFREF or HFPEF, and improved risk stratification are critical for the management and treatment of heart failure, but the proposed system does not address this.
The proposed system is also incapable of generating a prognosis based on the identified heart disease, and would instead require a cardiologist to manually form the prognosis.
Accordingly, there is a need for an improved and fully automatic clinical workflow that recognizes and analyzes both 2D and Doppler modality echocardiographic images for automated measurements and the diagnosis, prediction and prognosis of heart disease.